Prediction of welding quality characteristics during pulsed GTAW process of aluminum alloy by multisensory fusion and hybrid network model

Abstract

To implement predicting and controlling of welding quality are significant during pulsed gas tungsten arc welding (GTAW) process. In this paper, a multi-sensor system has been developed to synchronously obtain arc voltage, welding current, arc power, arc sound and weld pool images during pulsed GTAW process. The convolutional neural network (CNN) is designed to extract the visual feature of weld pool images. Besides, the time-frequency domain features of arc voltage, welding current, arc power, arc sound are also extracted. These features constituted a 19-dimensional feature vector. The long short-term memory (LSTM) network is used to fuse the extracted 19-dimensional features and learn time series information from the fused features. Further, the LSTM network can predict the different welding states 0−2 s in advance: normal penetration, lack of fusion, sag depression, burn through and misalignment. Finally, the proposed hybrid network model, CNN-LSTM, is verified to be effective with high accuracy and robustness.